Visible to the public Biblio

Found 9603 results

2020-08-03
Juuti, Mika, Szyller, Sebastian, Marchal, Samuel, Asokan, N..  2019.  PRADA: Protecting Against DNN Model Stealing Attacks. 2019 IEEE European Symposium on Security and Privacy (EuroS P). :512–527.
Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality of ML models becomes paramount for two reasons: (a) a model can be a business advantage to its owner, and (b) an adversary may use a stolen model to find transferable adversarial examples that can evade classification by the original model. Access to the model can be restricted to be only via well-defined prediction APIs. Nevertheless, prediction APIs still provide enough information to allow an adversary to mount model extraction attacks by sending repeated queries via the prediction API. In this paper, we describe new model extraction attacks using novel approaches for generating synthetic queries, and optimizing training hyperparameters. Our attacks outperform state-of-the-art model extraction in terms of transferability of both targeted and non-targeted adversarial examples (up to +29-44 percentage points, pp), and prediction accuracy (up to +46 pp) on two datasets. We provide take-aways on how to perform effective model extraction attacks. We then propose PRADA, the first step towards generic and effective detection of DNN model extraction attacks. It analyzes the distribution of consecutive API queries and raises an alarm when this distribution deviates from benign behavior. We show that PRADA can detect all prior model extraction attacks with no false positives.
Parmar, Manisha, Domingo, Alberto.  2019.  On the Use of Cyber Threat Intelligence (CTI) in Support of Developing the Commander's Understanding of the Adversary. MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM). :1–6.
Cyber Threat Intelligence (CTI) is a rapidly developing field which has evolved in direct response to exponential growth in cyber related crimes and attacks. CTI supports Communication and Information System (CIS)Security in order to bolster defenses and aids in the development of threat models that inform an organization's decision making process. In a military organization like NATO, CTI additionally supports Cyberspace Operations by providing the Commander with essential intelligence about the adversary, their capabilities and objectives while operating in and through cyberspace. There have been many contributions to the CTI field; a noteworthy contribution is the ATT&CK® framework by the Mitre Corporation. ATT&CK® contains a comprehensive list of adversary tactics and techniques linked to custom or publicly known Advanced Persistent Threats (APT) which aids an analyst in the characterization of Indicators of Compromise (IOCs). The ATT&CK® framework also demonstrates possibility of supporting an organization with linking observed tactics and techniques to specific APT behavior, which may assist with adversary characterization and identification, necessary steps towards attribution. The NATO Allied Command Transformation (ACT) and the NATO Communication and Information Agency (NCI Agency) have been experimenting with the use of deception techniques (including decoys) to increase the collection of adversary related data. The collected data is mapped to the tactics and techniques described in the ATT&CK® framework, in order to derive evidence to support adversary characterization; this intelligence is pivotal for the Commander to support mission planning and determine the best possible multi-domain courses of action. This paper describes the approach, methodology, outcomes and next steps for the conducted experiments.
Xiong, Chen, Chen, Hua, Cai, Ming, Gao, Jing.  2019.  A Vehicle Trajectory Adversary Model Based on VLPR Data. 2019 5th International Conference on Transportation Information and Safety (ICTIS). :903–912.
Although transport agency has employed desensitization techniques to deal with the privacy information when publicizing vehicle license plate recognition (VLPR) data, the adversaries can still eavesdrop on vehicle trajectories by certain means and further acquire the associated person and vehicle information through background knowledge. In this work, a privacy attacking method by using the desensitized VLPR data is proposed to link the vehicle trajectory. First the road average speed is evaluated by analyzing the changes of traffic flow, which is used to estimate the vehicle's travel time to the next VLPR system. Then the vehicle suspicion list is constructed through the time relevance of neighboring VLPR systems. Finally, since vehicles may have the same features like color, type, etc, the target trajectory will be located by filtering the suspected list by the rule of qualified identifier (QI) attributes and closest time method. Based on the Foshan City's VLPR data, the method is tested and results show that correct vehicle trajectory can be linked, which proves that the current VLPR data publication way has the risk of privacy disclosure. At last, the effects of related parameters on the proposed method are discussed and effective suggestions are made for publicizing VLPR date in the future.
Ferraris, Davide, Fernandez-Gago, Carmen, Daniel, Joshua, Lopez, Javier.  2019.  A Segregated Architecture for a Trust-based Network of Internet of Things. 2019 16th IEEE Annual Consumer Communications Networking Conference (CCNC). :1–6.
With the ever-increasing number of smart home devices, the issues related to these environments are also growing. With an ever-growing attack surface, there is no standard way to protect homes and their inhabitants from new threats. The inhabitants are rarely aware of the increased security threats that they are exposed to and how to manage them. To tackle this problem, we propose a solution based on segmented architectures similar to the ones used in industrial systems. In this approach, the smart home is segmented into various levels, which can broadly be categorised into an inner level and external level. The external level is protected by a firewall that checks the communication from/to the Internet to/from the external devices. The internal level is protected by an additional firewall that filters the information and the communications between the external and the internal devices. This segmentation guarantees a trusted environment among the entities of the internal network. In this paper, we propose an adaptive trust model that checks the behaviour of the entities and in case the entities violate trust rules they can be put in quarantine or banned from the network.
Moradi, Ashkan, Venkategowda, Naveen K. D., Werner, Stefan.  2019.  Coordinated Data-Falsification Attacks in Consensus-based Distributed Kalman Filtering. 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). :495–499.
This paper considers consensus-based distributed Kalman filtering subject to data-falsification attack, where Byzantine agents share manipulated data with their neighboring agents. The attack is assumed to be coordinated among the Byzantine agents and follows a linear model. The goal of the Byzantine agents is to maximize the network-wide estimation error while evading false-data detectors at honest agents. To that end, we propose a joint selection of Byzantine agents and covariance matrices of attack sequences to maximize the network-wide estimation error subject to constraints on stealthiness and the number of Byzantine agents. The attack strategy is then obtained by employing block-coordinate descent method via Boolean relaxation and backward stepwise based subset selection method. Numerical results show the efficiency of the proposed attack strategy in comparison with other naive and uncoordinated attacks.
Qin, Xinghong, Li, Bin, Huang, Jiwu.  2019.  A New Spatial Steganographic Scheme by Modeling Image Residuals with Multivariate Gaussian Model. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :2617–2621.
Embedding costs used in content-adaptive image steganographic schemes can be defined in a heuristic way or with a statistical model. Inspired by previous steganographic methods, i.e., MG (multivariate Gaussian model) and MiPOD (minimizing the power of optimal detector), we propose a model-driven scheme in this paper. Firstly, we model image residuals obtained by high-pass filtering with quantized multivariate Gaussian distribution. Then, we derive the approximated Fisher Information (FI). We show that FI is related to both Gaussian variance and filter coefficients. Lastly, by selecting the maximum FI value derived with various filters as the final FI, we obtain embedding costs. Experimental results show that the proposed scheme is comparable to existing steganographic methods in resisting steganalysis equipped with rich models and selection-channel-aware rich models. It is also computational efficient when compared to MiPOD, which is the state-of-the-art model-driven method.
Nakayama, Kiyoshi, Muralidhar, Nikhil, Jin, Chenrui, Sharma, Ratnesh.  2019.  Detection of False Data Injection Attacks in Cyber-Physical Systems using Dynamic Invariants. 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). :1023–1030.
Modern cyber-physical systems are increasingly complex and vulnerable to attacks like false data injection aimed at destabilizing and confusing the systems. We develop and evaluate an attack-detection framework aimed at learning a dynamic invariant network, data-driven temporal causal relationships between components of cyber-physical systems. We evaluate the relative performance in attack detection of the proposed model relative to traditional anomaly detection approaches. In this paper, we introduce Granger Causality based Kalman Filter with Adaptive Robust Thresholding (G-KART) as a framework for anomaly detection based on data-driven functional relationships between components in cyber-physical systems. In particular, we select power systems as a critical infrastructure with complex cyber-physical systems whose protection is an essential facet of national security. The system presented is capable of learning with or without network topology the task of detection of false data injection attacks in power systems. Kalman filters are used to learn and update the dynamic state of each component in the power system and in-turn monitor the component for malicious activity. The ego network for each node in the invariant graph is treated as an ensemble model of Kalman filters, each of which captures a subset of the node's interactions with other parts of the network. We finally also introduce an alerting mechanism to surface alerts about compromised nodes.
Chowdhary, Ankur, Sengupta, Sailik, Alshamrani, Adel, Huang, Dijiang, Sabur, Abdulhakim.  2019.  Adaptive MTD Security using Markov Game Modeling. 2019 International Conference on Computing, Networking and Communications (ICNC). :577–581.
Large scale cloud networks consist of distributed networking and computing elements that process critical information and thus security is a key requirement for any environment. Unfortunately, assessing the security state of such networks is a challenging task and the tools used in the past by security experts such as packet filtering, firewall, Intrusion Detection Systems (IDS) etc., provide a reactive security mechanism. In this paper, we introduce a Moving Target Defense (MTD) based proactive security framework for monitoring attacks which lets us identify and reason about multi-stage attacks that target software vulnerabilities present in a cloud network. We formulate the multi-stage attack scenario as a two-player zero-sum Markov Game (between the attacker and the network administrator) on attack graphs. The rewards and transition probabilities are obtained by leveraging the expert knowledge present in the Common Vulnerability Scoring System (CVSS). Our framework identifies an attacker's optimal policy and places countermeasures to ensure that this attack policy is always detected, thus forcing the attacker to use a sub-optimal policy with higher cost.
Li, Guanyu, Zhang, Menghao, Liu, Chang, Kong, Xiao, Chen, Ang, Gu, Guofei, Duan, Haixin.  2019.  NETHCF: Enabling Line-rate and Adaptive Spoofed IP Traffic Filtering. 2019 IEEE 27th International Conference on Network Protocols (ICNP). :1–12.
In this paper, we design NETHCF, a line-rate in-network system for filtering spoofed traffic. NETHCF leverages the opportunity provided by programmable switches to design a novel defense against spoofed IP traffic, and it is highly efficient and adaptive. One key challenge stems from the restrictions of the computational model and memory resources of programmable switches. We address this by decomposing the HCF system into two complementary components-one component for the data plane and another for the control plane. We also aggregate the IP-to-Hop-Count (IP2HC) mapping table for efficient memory usage, and design adaptive mechanisms to handle end-to-end routing changes, IP popularity changes, and network activity dynamics. We have built a prototype on a hardware Tofino switch, and our evaluation demonstrates that NETHCF can achieve line-rate and adaptive traffic filtering with low overheads.
Liu, Meng, Wang, Longbiao, Dang, Jianwu, Nakagawa, Seiichi, Guan, Haotian, Li, Xiangang.  2019.  Replay Attack Detection Using Magnitude and Phase Information with Attention-based Adaptive Filters. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :6201–6205.
Automatic Speech Verification (ASV) systems are highly vulnerable to spoofing attacks, and replay attack poses the greatest threat among various spoofing attacks. In this paper, we propose a novel multi-channel feature extraction method with attention-based adaptive filters (AAF). Original phase information, discarded by conventional feature extraction techniques after Fast Fourier Transform (FFT), is promising in distinguishing genuine from replay spoofed speech. Accordingly, phase and magnitude information are respectively extracted as phase channel and magnitude channel complementary features in our system. First, we make discriminative ability analysis on full frequency bands with F-ratio methods. Then attention-based adaptive filters are implemented to maximize capturing of high discriminative information on frequency bands, and the results on ASVspoof 2017 challenge indicate that our proposed approach achieved relative error reduction rates of 78.7% and 59.8% on development and evaluation dataset than the baseline method.
Xin, Le, Li, Yuanji, Shang, Shize, Li, Guangrui, Yang, Yuhao.  2019.  A Template Matching Background Filtering Method for Millimeter Wave Human Security Image. 2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR). :1–6.
In order to solve the interference of burrs, aliasing and other noises in the background area of millimeter wave human security inspection on the objects identification, an adaptive template matching filtering method is proposed. First, the preprocessed original image is segmented by level set algorithm, then the result is used as a template to filter the background of the original image. Finally, the image after background filtered is used as the input of bilateral filtering. The contrast experiments based on the actual millimeter wave image verifies the improvement of this algorithm compared with the traditional filtering method, and proves that this algorithm can filter the background noise of the human security image, retain the image details of the human body area, and is conducive to the object recognition and location in the millimeter wave security image.
LiPing, Yuan, Pin, Han.  2019.  Research of Low-Quality Laser Security Code Enhancement Technique. 2019 Chinese Automation Congress (CAC). :793–796.
The laser security code has been widely used for providing guarantee for ensuring quality of productions and maintaining market circulation order. The laser security code is printed on the surface of the productions, and it may be disturbed by printing method, printing position, package texture and background, which will make the laser security code cannot work normally. The image enhancement algorithm combining with bilateral filter and contrast limited adaptive histogram equalization is provided, which can realize the enhanced display of laser security code in strong interference background. The performance of this algorithm is analyzed and evaluated by experiments, and it can prove that the indexes of this algorithm are better than others.
Saxena, Shubhankar, Jais, Rohan, Hota, Malaya Kumar.  2019.  Removal of Powerline Interference from ECG Signal using FIR, IIR, DWT and NLMS Adaptive Filter. 2019 International Conference on Communication and Signal Processing (ICCSP). :0012–0016.
ECG signals are often corrupted by 50 Hz noise, the frequency from the power supply. So it becomes quite necessary to remove Power Line Interference (PLI) from the ECG signal. The reference ECG signal data was taken from the MIT-BIH database. Different filtering techniques comprising of Discrete Wavelet Transform (DWT), Normalized Least Mean Square (NLMS) filter, Finite Impulse Response (FIR) filter and Infinite Impulse Response (IIR) filter were used in this paper for denoising the ECG signal which was corrupted by the PLI. Later, the comparison was made among the methods, to find the best methodology to denoise the corrupted ECG signal. The parameters that were used for the comparison are Mean Square Error (MSE), Mean Absolute Error (MAE), Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR). Higher values of SNR & PSNR and lower values of MSE & MAE define the best denoising algorithm.
Shu-fen, NIU, Bo-bin, WANG, You-chen, WANG, Jin-feng, WANG, Jing-min, CHEN.  2019.  Efficient and Secure Proxy re-signature Message Authentication Scheme in Vehicular Ad Hoc Network. 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). :1652–1656.

In order to solve privacy protection problem in the Internet of Vehicles environment, a message authentication scheme based on proxy re-signature is proposed using elliptic curves, which realizes privacy protection by transforming the vehicle's signature of the message into the roadside unit's signature of the same message through the trusted center. And through the trusted center traceability, to achieve the condition of privacy protection, and the use of batch verification technology, greatly improve the efficiency of authentication. It is proved that the scheme satisfies unforgeability in ECDLP hard problem in the random oracle model. The efficiency analysis shows that the scheme meets the security and efficiency requirements of the Internet of Vehicles and has certain practical significance.

Prasad, Mahendra, Tripathi, Sachin, Dahal, Keshav.  2019.  Wormhole attack detection in ad hoc network using machine learning technique. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–7.

In this paper, we explore the use of machine learning technique for wormhole attack detection in ad hoc network. This work has categorized into three major tasks. One of our tasks is a simulation of wormhole attack in an ad hoc network environment with multiple wormhole tunnels. A next task is the characterization of packet attributes that lead to feature selection. Consequently, we perform data generation and data collection operation that provide large volume dataset. The final task is applied to machine learning technique for wormhole attack detection. Prior to this, a wormhole attack has detected using traditional approaches. In those, a Multirate-DelPHI is shown best results as detection rate is 90%, and the false alarm rate is 20%. We conduct experiments and illustrate that our method performs better resulting in all statistical parameters such as detection rate is 93.12% and false alarm rate is 5.3%. Furthermore, we have also shown results on various statistical parameters such as Precision, F-measure, MCC, and Accuracy.

Arthi, A., Aravindhan, K..  2019.  Enhancing the Performance Analysis of LWA Protocol Key Agreement in Vehicular Ad hoc Network. 2019 5th International Conference on Advanced Computing Communication Systems (ICACCS). :1070–1074.

Road accidents are challenging threat in the present scenario. In India there are 5, 01,423 road accidents in 2015. A day 400 hundred deaths are forcing to India to take car safety sincerely. The common cause for road accidents is driver's distraction. In current world the people are dominated by the tablet PC and other hand held devices. The VANET technology is a vehicle-to-vehicle communication; here the main challenge will be to deliver qualified communication during mobility. The paper proposes a standard new restricted lightweight authentication protocol utilizing key agreement theme for VANETs. Inside the planned topic, it has three sorts of validations: 1) V2V 2) V2CH; and 3) CH and RSU. Aside from this authentication, the planned topic conjointly keeps up mystery keys between RSUs for the safe communication. Thorough informal security analysis demonstrates the planned subject is skilled to guard different malicious attack. In addition, the NS2 Simulation exhibits the possibility of the proposed plan in VANET background.

Gopalakrishnan, S., Rajesh, A..  2019.  Cluster based Intrusion Detection System for Mobile Ad-hoc Network. 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). 1:11–15.

Mobile Ad-hoc network is decentralized and composed of various individual devices for communicating with each other. Its distributed nature and infrastructure deficiency are the way for various attacks in the network. On implementing Intrusion detection systems (IDS) in ad-hoc node securities were enhanced by means of auditing and monitoring process. This system is composed with clustering protocols which are highly effective in finding the intrusions with minimal computation cost on power and overhead. The existing protocols were linked with the routes, which are not prominent in detecting intrusions. The poor route structure and route renewal affect the cluster hardly. By which the cluster are unstable and results in maximization processing along with network traffics. Generally, the ad hoc networks are structured with battery and rely on power limitation. It needs an active monitoring node for detecting and responding quickly against the intrusions. It can be attained only if the clusters are strong with extensive sustaining capability. Whenever the cluster changes the routes also change and the prominent processing of achieving intrusion detection will not be possible. This raises the need of enhanced clustering algorithm which solved these drawbacks and ensures the network securities in all manner. We proposed CBIDP (cluster based Intrusion detection planning) an effective clustering algorithm which is ahead of the existing routing protocol. It is persistently irrespective of routes which monitor the intrusion perfectly. This simplified clustering methodology achieves high detecting rates on intrusion with low processing as well as memory overhead. As it is irrespective of the routes, it also overcomes the other drawbacks like traffics, connections and node mobility on the network. The individual nodes in the network are not operative on finding the intrusion or malicious node, it can be achieved by collaborating the clustering with the system.

POLAT, Hüseyin, POLAT, Onur, SÖĞÜT, Esra, ERDEM, O. Ayhan.  2019.  Performance Analysis of Between Software Defined Wireless Network and Mobile Ad Hoc Network Under DoS Attack. 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). :1–5.

The traditional network used today is unable to meet the increasing needs of technology in terms of management, scaling, and performance criteria. Major developments in information and communication technologies show that the traditional network structure is quite lacking in meeting the current requirements. In order to solve these problems, Software Defined Network (SDN) is capable of responding as it, is flexible, easier to manage and offers a new structure. Software Defined Networks have many advantages over traditional network structure. However, it also brings along many security threats due to its new architecture. For example, the DoS attack, which overloads the controller's processing and communication capacity in the SDN structure, is a significant threat. Mobile Ad Hoc Network (MANET), which is one of the wireless network technologies, is different from SDN technology. MANET is exposed to various attacks such as DoS due to its security vulnerabilities. The aim of the study is to reveal the security problems in SDN structure presented with a new understanding. This is based on the currently used network structures such as MANET. The study consists of two parts. First, DoS attacks against the SDN controller were performed. Different SDN controllers were used for more accurate results. Second, MANET was established and DoS attacks against this network were performed. Different MANET routing protocols were used for more accurate results. According to the scenario, attacks were performed and the performance values of the networks were tested. The reason for using two different networks in this study is to compare the performance values of these networks at the time of attack. According to the test results, both networks were adversely affected by the attacks. It was observed that network performance decreased in MANET structure but there was no network interruption. The SDN controller becomes dysfunctional and collapses as a result of the attack. While the innovations offered by the SDN structure are expected to provide solutions to many problems in traditional networks, there are still many vulnerabilities for network security.

Seetharaman, R., Subramaniam, L.Harihara, Ramanathan, S..  2019.  Mobile Ad Hoc Network for Security Enhancement. 2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC). :279–282.

This project enhances the security in which Ad Hoc On-Demand Distance Vector (AODV) routing protocol for MANETs with the game theoretical approach. This is achieved by using public key and private key for encryption and decryption processes. Proactive and reactive method is implemented in the proposed system. Reactive method is done in identification process but in proactive method is used to identify the nodes and also block the hackers node, then change the direction of data transmission to good nodes. This application can be used in military, research, confidential and emergency circumferences.

Yang, Xiaodong, Liu, Rui, Wang, Meiding, Chen, Guilan.  2019.  Identity-Based Aggregate Signature Scheme in Vehicle Ad-hoc Network. 2019 4th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). :1046–10463.

Vehicle ad-hoc network (VANET) is the main driving force to alleviate traffic congestion and accelerate the construction of intelligent transportation. However, the rapid growth of the number of vehicles makes the construction of the safety system of the vehicle network facing multiple tests. This paper proposes an identity-based aggregate signature scheme to protect the privacy of vehicle identity, receive messages in time and authenticate quickly in VANET. The scheme uses aggregate signature algorithm to aggregate the signatures of multiple users into one signature, and joins the idea of batch authentication to complete the authentication of multiple vehicular units, thereby improving the verification efficiency. In addition, the pseudoidentity of vehicles is used to achieve the purpose of vehicle anonymity and privacy protection. Finally, the secure storage of message signatures is effectively realized by using reliable cloud storage technology. Compared with similar schemes, this paper improves authentication efficiency while ensuring security, and has lower storage overhead.

Maxa, Jean-Aimé, Ben Mahmoud, Mohamed Slim, Larrieu, Nicolas.  2019.  Performance evaluation of a new secure routing protocol for UAV Ad hoc Network. 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC). :1–10.

UAANET (UAV Ad hoc Network) is defined as an autonomous system made of swarm of UAVs (Unmanned Aerial Vehicle) and GCS (Ground Control Station). Compared to other types of MANET (Mobile Ad hoc network), UAANET have some unique features and bring several challenges. One of them is the design of routing protocol. It must be efficient for creating routes between nodes and dynamically adjusting to the rapidly changing topology. It must also be secure to protect the integrity of the network against malicious attackers. In this paper, we will present the architecture and the performance evaluation (based on both real-life experimental and emulation studies) of a secure routing protocol called SUAP (Secure UAV Ad hoc routing Protocol). SUAP ensures routing services between nodes to exchange real-time traffic and also guarantees message authentication and integrity to protect the network integrity. Additional security mechanisms were added to detect Wormhole attacks. Wormhole attacks represent a high level of risk for UAV ad hoc network and this is the reason why we choose to focus on this specific multi node attack. Through performance evaluation campaign, our results show that SUAP ensures the expected security services against different types of attacks while providing an acceptable quality of service for real-time data exchanges.

Islam, Noman.  2019.  A Secure Service Discovery Scheme for Mobile ad hoc Network using Artificial Deep Neural Network. 2019 International Conference on Frontiers of Information Technology (FIT). :133–1335.

In this paper, an agent-based cross-layer secure service discovery scheme has been presented. Service discovery in MANET is a critical task and it presents numerous security challenges. These threats can compromise the availability, privacy and integrity of service discovery process and infrastructure. This paper highlights various security challenges prevalent to service discovery in MANET. Then, in order to address these security challenges, the paper proposes a cross-layer, agent based secure service discovery scheme for MANET based on deep neural network. The software agents will monitor the intrusive activities in the network based on an Intrusion Detection System (IDS). The service discovery operation is performed based on periodic dissemination of service, routing and security information. The QoS provisioning is achieved by encapsulating QoS information in the periodic advertisements done by service providers. The proposed approach has been implemented in JIST/ SWANS simulator. The results show that proposed approach provides improved security, scalability, latency, packet delivery ratio and service discovery success ratio, for various simulation scenarios.

Al-Emadi, Sara, Al-Ali, Abdulla, Mohammad, Amr, Al-Ali, Abdulaziz.  2019.  Audio Based Drone Detection and Identification using Deep Learning. 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC). :459–464.
In recent years, unmanned aerial vehicles (UAVs) have become increasingly accessible to the public due to their high availability with affordable prices while being equipped with better technology. However, this raises a great concern from both the cyber and physical security perspectives since UAVs can be utilized for malicious activities in order to exploit vulnerabilities by spying on private properties, critical areas or to carry dangerous objects such as explosives which makes them a great threat to the society. Drone identification is considered the first step in a multi-procedural process in securing physical infrastructure against this threat. In this paper, we present drone detection and identification methods using deep learning techniques such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Convolutional Recurrent Neural Network (CRNN). These algorithms will be utilized to exploit the unique acoustic fingerprints of the flying drones in order to detect and identify them. We propose a comparison between the performance of different neural networks based on our dataset which features audio recorded samples of drone activities. The major contribution of our work is to validate the usage of these methodologies of drone detection and identification in real life scenarios and to provide a robust comparison of the performance between different deep neural network algorithms for this application. In addition, we are releasing the dataset of drone audio clips for the research community for further analysis.
Liu, Fuxiang, Jiang, Qi.  2019.  Research on Recognition of Criminal Suspects Based on Foot Sounds. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :1347–1351.
There are two main contributions in this paper: Firstly, by analyzing the frequency domain features and Mel domain features, we can identify footstep events and non-footstep events. Secondly, we compared the two footstep sound signals of the same person in frequency domain under different experimental conditions, finding that almost all of their peak frequencies and trough frequencies in the main frequency band are respectively corresponding one-to-one. However for the two different people, even under the same experimental conditions, it is difficult to have the same peak frequencies and trough frequencies in the main frequency band of their footstep sound signals. Therefore, this feature of footstep sound signals can be used to identify different people.
Dai, Haipeng, Liu, Alex X., Li, Zeshui, Wang, Wei, Zhang, Fengmin, Dong, Chao.  2019.  Recognizing Driver Talking Direction in Running Vehicles with a Smartphone. 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). :10–18.
This paper addresses the fundamental problem of identifying driver talking directions using a single smartphone, which can help drivers by warning distraction of having conversations with passengers in a vehicle and enable safety enhancement. The basic idea of our system is to perform talking status and direction identification using two microphones on a smartphone. We first use the sound recorded by the two microphones to identify whether the driver is talking or not. If yes, we then extract the so-called channel fingerprint from the speech signal and classify it into one of three typical driver talking directions, namely, front, right and back, using a trained model obtained in advance. The key novelty of our scheme is the proposition of channel fingerprint which leverages the heavy multipath effects in the harsh in-vehicle environment and cancels the variability of human voice, both of which combine to invalidate traditional TDoA, DoA and fingerprint based sound source localization approaches. We conducted extensive experiments using two kinds of phones and two vehicles for four phone placements in three representative scenarios, and collected 23 hours voice data from 20 participants. The results show that our system can achieve 95.0% classification accuracy on average.